@Article{cmc.2026.086571,
AUTHOR = {Fen Liu, Jinghua Zhang, Weijie Tan},
TITLE = {STP-BTDM: Semi-Tensor Product-Based Block Term Decomposition of Multilinear Pooling Method for Multi-Modal Information Fusion in Sentiment Analysis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {89},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v89n2/68825},
ISSN = {1546-2226},
ABSTRACT = {Multi-modal information fusion integrates data from various sensors, distinct sources, or different modalities, such as audio, images, and text, to achieve a more comprehensive and accurate understanding and analysis. This paper proposes a Semi-Tensor Product-based Block Term Decomposition of Multilinear (STP-BTDM) pooling method and applies it to sentiment analysis and emotion recognition. Unlike prior factorized multilinear approaches, STP-BTDM introduces block-term decomposition with a block-diagonal core tensor, yielding a globally sparse yet locally dense structure and enabling modality-specific independent subspace learning. The technique first introduces the Semi-Tensor Product-based Block Term Decomposition (STP-BTD) model to obtain globally sparse and locally dense weight tensors. Subsequently, by combining the multilinear pooling model, the STP-BTDM method is presented. This approach allows each modality to be controlled by only one block of the block-diagonal core, with different blocks being independent during training. The model can represent full multilinear interactions in a computationally efficient manner. Moreover, the introduction of sparsity constraints in the core tensor of STP-BTDM enhances the generalization performance of multilinear pooling. The resulting locally dense yet globally sparse characteristics make the model highly flexible. Finally, our experiments with the STP-BTDM method on the CMU-MOSI dataset for sentiment analysis and the IEMOCAP dataset for emotion recognition demonstrate its superior performance and effectiveness across both tasks.},
DOI = {10.32604/cmc.2026.086571}
}